Weather Forecasting (next day's temperature) using historical weather data:

OVERVIEW :

Weather forecasting has become a very important application these days. It can help to reduce weather-related losses and enhance societal benefits, including protection of life and property, public health and safety, and support of economic prosperity and quality of life. Many activities are decided based on the weather on a particular day - like organizing an outdoor event, sports matches, construction activities, etc. Also, because of the varying climate changes across the planet, it has become imperative to know about the weather so that decisions to improve the climate can be taken. This is particularly useful for government institutions studying climate change. With an evolution in science and technology, we have been able to predict the weather with great accuracy. Here we propose to utilize a Machine Learning Model to predict weather based on the historical data obtained from the public dataset.

GOALS :

● Exploratory Data Analysis of the Weather attributes like Temperature and Precipitation for some of the cities in California ● Applying a Machine Learning Model and training the model using our Test Data from the above Dataset. ● Validating the Model and then checking the accuracy of the results.

TOOLS to be Used: We plan to use Python Programming with the various built in Libraries to study the dataset obtained from ‘National Centers for Environmental information’ and select and train the model.